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Entropy 2015, 17(12), 7996-8006; doi:10.3390/e17127858

An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique

1
Department of Mathematics and Statistics, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, Jordan
2
Departamento de Matemática Aplicada, Universidad de Granada, Granada 18071, Spain
3
Departamento de Física Aplicada, Universidad de Granada, Granada 18071, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 4 August 2015 / Revised: 11 November 2015 / Accepted: 24 November 2015 / Published: 4 December 2015
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory)
View Full-Text   |   Download PDF [9485 KB, uploaded 8 December 2015]   |  

Abstract

In this paper we develop a new procedure for entropic image edge detection. The presented method computes the Jensen–Shannon divergence of the normalized grayscale histogram of a set of multi-sized double sliding windows over the entire image. The procedure presents a good performance in images with textures, contrast variations and noise. We illustrate our procedure in the edge detection of medical images. View Full-Text
Keywords: entropy; Jensen–Shannon divergence; image segmentation; entropic edge detection; multi-size window processing entropy; Jensen–Shannon divergence; image segmentation; entropic edge detection; multi-size window processing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Katatbeh, Q.D.; Martínez-Aroza, J.; Gómez-Lopera, J.F.; Blanco-Navarro, D. An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique. Entropy 2015, 17, 7996-8006.

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